Post on 16-Jan-2016
Rural Health 2.0 and User-driven healthcare
Shoubhik Bose Engineer and independent researcher
Health 2.0 - What
Health 2.0 - Why
• Staying informed.
• Medical education.
• Collaboration and practice.
• Managing a particular disease.
• Sharing data for research.
Email v/s Collaboration
Health 2.0 - How
• telemedicine,
• electronic medical records
• mHealth
• Connected Health,
• The use of the internet by patients themselves such as througho messageboards, o blogs.
Rural health 2.0 Technology for low-infrastructure and
remote areas.
Blood test without bleeding ?
• Low-cost.
• No bleeding.
• Trained personnel not needed.
TouchHB
Invented by: Myshkin Ingawale
Anatomy of a social network
Tabula Rasa ( Facebook group )
User-driven healthcare : Our implementation
Mathabhanga district in West Bengal
Findings from a healthcare questionnaire
Absolute survey respondents V/S Commonly known problems
User-driven healthcare - What
User-driven healthcare- Why
• Individual patients should not have to get themselves shunted between multiple health professionals with multiple waiting times.
•The patient’s information travels through a network of dedicated health professionals rather than the patient having to travel physically.
User-driven healthcare- How
1. Collect health information.
1. Team of health experts discuss 'privatey'.
1. Produce a solution.
Dissecting the system
Screen grab: INPUT
Screen grab: OUTPUT
User-driven healthcare - features
• Hassle-free login using Google account.
• Multi-interface input.
• SMS and voice service from low-end phones ( from rural-seekers )
• Anonymised patient names.
• Secure discussion boards.
• Reports-hosting.
• Solution recommendation engine.
• Android Application.
The proposed flow
• Patient sends a voice/text message.
• Message is sent to the server and made available on the web page.
• Medics get notified by email/phone.
InputTIER 1
PATIENT CARE-GIVER
Input - The proposed (alternate) flow
Dial a number and narrate the problem over the phone.
Audio
Speech-to-text engine.
Text ( after translation )
UDHC
Inputs - methods
Scanned narratives
Transliteration in local language
Input - Transliteration
Some of the Patient inputs we have recieved over email/web so far.
The input - How do we anonymise?
• Scientific names used instead of real patient names.
• Mashed up with pincode for geolocation
• Blurring of names in reports manually using image editors before uploading.
ABELMOSCHUS480***ESCULENTUS
The input - Anonymisation( proposed )
1. Image editors native to the web-based application to allow image manipulation after upload.
1. "Identifier detection" algorithms to automatically remove patient identifiers.
1. Image tagging. ( Something similar to Facebook? )
Patient consent form ( English )
Patient Consent form
I give my consent for this information about MYSELF/MY WARD/MY RELATIVE [indicate correct description] relating to my/his/her health to appear in the ‘User Driven Health Care’ UDHC ‘clinical problem solving’ forum and web based electronic health record.
I understand the following:1. UDHC forum is an initiative to promote transparency and ethical practices in health care while qualitatively studying its processes to assess the role of
various (hitherto unidentified) factors that influence healthcare outcomes.
1. UDHC network aims to qualitatively study and in a controlled online-forum, support the phenomenon of sharing an individual's disease related information with other multiple stake holders in healthcare such as similar patients, related health and other professionals to determine if this shared learning activity is beneficial in the individual's healthcare outcome.
1. My information will be published without my real name attached and UDHC ‘clinical problem solving’ forum will make every attempt to ensure my anonymity addressing me solely by my anonymized user-name.
1. I understand, however, that complete anonymity cannot be guaranteed. It is possible that somebody somewhere - perhaps, for example, somebody who looked after me if I was in hospital or a relative - may identify me.
1. UDHC ‘clinical problem solving’ forum will not allow the Information to be used out of context. The text of the information may be edited for style, grammar, consistency, and length.
1. The Information may be published in the UDHC ‘clinical problem solving’ forum and associated journals on paper as well as in the internet, which would be distributed worldwide.
1. Information displayed in the UDHC ‘clinical problem solving’ forum is not supposed to replace advice from the primary care physician of the patient.
Signed Accept (Patient) Witness
Patient consent form ( Hindi )
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हस्�क्षरो / सहमति / असहमति
Inputs - representation [ list ]
Inputs - representation [ graphical ]
Inputs - pitfalls and workarounds
• Rural population has less/no access to the Internet.
• Language barrier – very few might know English.
• Computer illiteracy.
• How to use website forms?
• Narrate as voice using mobile phones.
• Transliteration.
(and even translation)
• Not a problem, mobile phones will do!
• Medical Kiosks administered by computer literate volunteers.
Hidden layerTIER 2
MODERATORS
MEDICAL STUDENTS
CARE-GIVERS
The hidden layer - discussion
OutputTIER 3
CARE-GIVERS CARE-SEEKERSMODERATORS
Evidence-based learning
Recommendation engine
• Patient posts health issue.
• System "predicts" similarity with a previously solved health case.
• Health experts take assistance of previously solved case to provide solution.
• The new solution gets added to the "repository" of previously solved cases.
Data from BMJ
Custom Algorithm:Tag-ifying the health narratives
Heuristic approach for predicting similarity
• Tag- ify
• Determine similarity between 2 narratives.o choose those which have a high number of similar
tags. o Threshold of the percentage of similarity.
• Test with 20% , 40% , 50% , 60% , 80% tags' similarity and analyse whether the algorithmic results match the logically expected results.
Artificial Intelligence Google Prediction algorithm
Quoting the Google Prediction algorithm
• Given a new item, predict a numeric value for that item, based on similar valued examples in its training data.
• Given a new item, choose a category that describes it best, given a set of similar categorized items in its training data.
"Google Prediction" for health narratives
Given a new health narrative ,
choose a health record that describes it best, given a set of similar health records items in its training data.
Given a new health narrative , predict a previously solved case from the archive, based on similar valued examples in its training data.
Scores from our Mathabhanga chapter where the UDHC team is led by Dr.Biswas.
August 2011 November 2012
150 health cases discussed over email
20 to 30 % patients travelled to Bhopal after initial consultation
Crowdsourcing the UDHC platform
Mr. Kar
Who can contribute ?
•Medical students.•Engineers.•Designers.•Doctors.•Moderators.•Translators.•Content editors.•Care-seekers
... and the list goes one.
Anyone can help make build this encyclopedia of health issues.
References
• Swan Health 2.0 model , Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking, Int. J. Environ. Res. Public Health 2009, 6, 492-525
• BMJ ( 2012 ), BMJ Case Reports - Article archive by date, Retrieved fromhttp://casereports.bmj.com/content/by/year
• Biosense Technologies (2012) , Myshkin Ingawale, Retrieved from http://unreasonableinstitute.org/profile/mingawale/
• UDHC ( 2012 ) Retrieved from http://care.udhc.co.in/STATICS/what.jsp
• Evidence based learning , 5 steps to evidence-based learning , http://www.ebbp.org/steps.html
• Anatomy of a social network, Dave Gray, https://plus.google.com/117373186752666867801/posts/CQRVeKEsUvF
Thank you ,Questions and comments ?You can also get in touch Email: sbose78@gmail.comBlog: code-cooker.blogspot.comTwitter: sbose78Facebook: shoubhik.bose
UDHC website : care.udhc.co.in